Predictive Modeling Applications in Actuarial Science
- Volume 1
- Introduction
- Predictive Modeling Foundations
- Predictive Modeling Methods
- Bayesian and Mixed Modeling
- Longitudinal Modeling
- Volume 2
- Generalized Linear Model
- Extensions of the Generalized Linear Model
- Unsupervised Predictive Modeling Methods
-
Applications on Current Problems in Actuarial Science
- Chapter 8 - The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
- Chapter 9 - Finite Mixture Model and Workers’ Compensation Large-Loss Regression Analysis
- Chapter 10 - A Framework for Managing Claim Escalation Using Predictive Modeling
- Chapter 11 - Predictive Modeling for Usage-Based Auto Insurance
Chapter 11 - Predictive Modeling for Usage-Based Auto Insurance
Authors
Udi Makov | Verisk Insurance Solutions
udimakov@gmail.com
Jim Weiss | ISO, a Verisk Analytics company
Jweiss@iso.com
Chapter Preview
This chapter discusses a type of predictive modeling application commonly referred to as claims triage. The broad objective of claims triage is to use the characteristics of each individual claim at a specific point in time to predict some future outcome, which then dictates how the claim will be handled. In practice, claims triage might identify simpler claims for fast-track processing or alternatively identify complex claims that require expert handling or intervention. Claims triage models can help assign claims to the right adjuster or inform the adjuster of what actions to take (e.g., when to dispatch an engineer to a claim site or when to assign a nurse case manager to a workers’ compensation claim).Usage-based auto insurance, also known as UBI, involves analyzing data collected from policyholders’ vehicles via telematics to help determine premium rates. Behavioral information considered includes vehicles’ speeds, maneuvers, routes, mileage, and times of day of operation. UBI has been described as a potentially significant advancement over traditional techniques that rely on information such as policyholders’ ages as proxies for how riskily they drive. However, because data collected via telematics are volatile and voluminous, particular care must be taken by actuaries and data scientists when applying predictive modeling techniques to avoid over fitting or nonconvergence and to improve predictive power. In this chapter, we use a case study to evaluate how modeling techniques perform in a UBI environment and how various challenges may be addressed.